Machine Learning App Development Company
Agicent's ML development work covers the full range of what it takes to get a machine learning application from idea to production and keep it performing once it is live.
ML Consulting and Use Case Scoping
Agicent's engineers and business analysts align the proposed implementation of the ML use case with a measurable business outcome before any model development work begins. This isn't a discovery formality. It's where we clarify what data you actually have versus what you think you have, and what additional data needs to be collected.
We also define the success metrics the model will be evaluated against before and after deployment, and produce an execution roadmap with realistic timelines and data readiness requirements. If the data is not in a condition conducive to the model you're envisioning, we identify that here, not after six weeks of training runs.
Custom ML Model Development
We develop machine learning model from scratch: from data preparation to feature engineering, algorithm choice, training, model evaluation and further iterations of training and testing until the model meets the metrics we have agreed during the scoping process. The type of framework used is based on the data reality of the client.
In many enterprise scenarios, a gradient-boosted tree on a clean structured dataset with 50k rows will perform better in terms of accuracy and maintenance cost than a deep learning model, and we wouldn't make the choice to use the latest thing. Our ML engineers work on PyTorch, TensorFlow, Scikit-learn, XGBoost and Hugging Face depending on the problem at hand.
Neural Network and Deep Learning Development
In complex problems such as image classification, video analysis, long-range sequence modeling, and large-scale language understanding, where classic ML is just not on par with the problem data, Agicent trains CNN, RNN, LSTM and transformer architectures on GPUs powered training infrastructure.
We also apply model compression and knowledge distillation techniques for clients with edge deployment requirements, where inference needs to run on hardware with constrained memory and compute rather than a cloud endpoint.
Natural Language Processing (NLP) Development
Agicent develops NLP tools for Document Classification, Named Entity Recognition, Sentiment Analysis, Automatic Summarization, and Semantic Search. The architecture will be based on the specified task and deployment scenario: a highly specialized transformer for domain-specific extraction, embedding-based retrieval for semantic search, or a RAG pipeline with the model using a real knowledge base to validate its outputs instead of parametric memory.
Additionally, we develop the conversational interfaces and the AI Chatbots that are atop these NLP layers, for those clients who want a user-facing product rather than a back-end pipeline.
Computer Vision Development
The computer vision applications that we have worked on include quality control inspection, defect detection in manufacturing settings, document digitisation and OCR, retail shelf analytics and security monitoring applications. The common thread across these is that the model's precision and recall thresholds are not set against some benchmark score but rather based on the business impact of an error.
A defect missed on a production line and a false positive in a document classification workflow have very different downstream costs, and that informs how we set the operating threshold and design the human review layer.
Predictive Analytics and Demand Forecasting
Time-series forecasting, churn prediction, credit scoring, and demand planning models, which we built and deployed as APIs, can easily be integrated with existing ERP, CRM, or BI systems. The output of every predictive model Agicent ships is designed for the business user who acts on it, not just the engineer who built it.
That means explainability is part of the delivery: the model tells you what it predicted and, in terms a non-data-scientist can act on, why. And this is mandatory for regulated industries.
Recommendation Engine Development
We develop recommendation systems that employ collaborative filtering, content-based filtering, and hybrid (mixed) approaches for e-commerce, SaaS, media, and edtech applications. We work on the problems that make recommenders difficult in the real world: cold-start - when users or items are new; bias correction - when the training data is biased towards a set of user behaviours; and real-time serving at sub-100ms latency without sacrificing ranking quality. Use cases include product ranking, content surfacing, push notification personalisation and cross-sell and upsell logic integrated as part of existing product flows.
Intelligent Process Automation (IPA)
Rules based RPA fails in scenarios where the input is variable or unstructured. Agicent builds the ML layer that handles what rules cannot: classifying a scanned document that doesn't match a known template, extracting a field value from an invoice formatted differently by each vendor, and routing a support ticket based on intent rather than keyword.
The ML output is connected to the deterministic downstream action layer. There is no binary division of labor on how many places ML stops and how many places it starts the rules-based automation. That's part of the scoping process, and not something determined in development.
Machine Learning as a Service (MLaaS)
We provide managed cloud ML deployment options on AWS SageMaker, Azure Machine Learning, and GCP Vertex AI options for product teams that require production ML capability without the development of an internal infrastructure.
From the outset of an engagement, auto-scaling inference, usage monitoring, and scheduled retraining are included. It's not a transfer of documentation to a cloud vendor's documentation - the Agicent engineers deploy it, monitor it, and set up the retraining pipeline on the client's behalf.
MLOps and Model Lifecycle Management
An accurate model at deployment degrades as time goes on - data distributions shift, feature pipelines break, and user behaviour changes. None of that is apparent until a business metric begins to go in the wrong direction and the cause is not understood until a monitoring and governance layer is added.
We create model registries and automate CI/CD pipelines to release models, monitor for drift to trigger alerts as needed for each model's rate of degradation, and build version-controlled registries. This is an infrastructure and part of our every engagement scope and not a premium add-on.
LLM Fine-Tuning and Adaptation
Agicent optimises general purpose foundation models for use in specific tasks where the model's standard outputs are inadequate, such as legal document analysis, clinical note summarisation, financial report extraction, and domain-specific customer support.
For clients with a limited number of GPUs, we employ parameter-efficient fine-tuning techniques such as LoRA and QLoRA when full fine-tuning isn't cost effective. If embedding domain knowledge into model weights is impractical, we do not recommend fine-tuning; we build an RAG system instead of recommending fine-tuning for its own sake.
AI Agent Development for ML Workflows
We develop agentic ML systems, where the ML model is not merely a prediction but a series of actions through various tools and data sources to accomplish a task. Use cases range from automated research pipelines to multi-source data reconciliation, exception triage in an operations workflow, to a delegation chain of decisions that only requires a human to be in the loop at specific escalation points.
They are designed to solve the engineering challenges of agent failure in production: context window management across long sequences of tasks, error recovery when agents are called to a tool that fails, and a transparent handoff to human review when agents reach a point in the process they are unable to reliably complete.
Data Engineering for ML Pipelines
Machine learning models are only as accurate as the data they are trained on, and in most enterprise situations, the data is not as clean as the stakeholders would like. Agicent's data engineering practice constructs and audits ETL pipelines and creates feature stores to ensure consistency between the data used to train the model and the data it is used to predict on.
It addresses class imbalance and missing data, then creates a continuously running data quality monitoring process along with data model monitoring processes in production. This is typically the engagement that must occur before any work on a model can start for clients with data systems spread across a variety of systems.
Agicent has a five-step delivery process for all ML engagements. Phases are sequential, but not waterfall; there is feedback between these phases - for example, data found in Phase 2 might impact the architecture decisions in Phase 3.
Discovery and Business Mapping
Prior to any data being interacted with, Agicent's engineers and business analysts collaborate with the client team to align each ML use case with a discrete, measurable business outcome. We find integration points with current systems, determine what data is available and what will need to be gathered or labeled, and establish what metrics the model will be evaluated against, not only during training but also during production. If the conditions for a useful model are not present, we say so here. Rework done as a result of discovery findings saves much more time than it costs.
Data Engineering and Preparation
Most of the accuracy ceiling is set in data engineering and preparation. The choices made in this step will influence whether the model can use a lot of the signal contained in the data. We construct or audit ETL pipelines, apply suitable class imbalance strategies (not oversampling), design augmentation for low-volume data and set up data quality validation checks prior to every training job. This is when PII handling is complete, anonymisation needs are considered and documented, and data governance is documented.
Model Architecture, Training, and Evaluation
Model architecture, training, and evaluation follow from the data that it supports. We experiment with candidate model architectures, log metrics for every training run in MLflow, and test the final model on holdout sets with the same distribution as the data the model will be deployed on, which is often not the same as the distribution of the training data set, especially for time-series applications and applications with an evolving user base.
Integration and Deployment
Many technically successful ML projects fail in the integration and deployment phase. Agicent is responsible for packaging the API, tuning and profiling it for latency, load testing it at the expected request volume in production, and for the CI/CD pipeline that controls the process of releasing model updates without disrupting the application that relies on them. Depending on the connectivity and latency requirements of a specific application, we can support REST API deployments, event-driven inference architectures, and edge deployments.
MLOps, Monitoring, and Continuous Improvement
The deployment marks the beginning of MLOps, monitoring and continuous improvement and it has no end date. Agicent's monitoring layer monitors data drift, prediction drift and infrastructure health. Retraining jobs occur when they are needed, not when it is convenient. Reporting is designed to allow the business stakeholders who invested in the model to easily determine if the model is still performing the function it was designed for.
Scowtt - AI-driven predictive sales and marketing intelligence
Scowtt was looking to get a production-grade solution that would enable enterprise sales teams to prioritize leads for immediate follow-up rather than relying on past sales reports. The team wanted something that could provide enterprise sales teams with a production-grade platform that provides them with leads they can focus on today, not by past sales reporting, but by the predictive signals they receive.
We designed the entire product, including CRM Data ingestion, integration with their proprietary in-house LML scoring models, Next.js UI, and Node.js event-driven backend running on Google Cloud and AWS Lambda. More robust integrations were then made with Salesforce, HubSpot, and Zoho, as well as feeds to paid ad platforms for real-time targeting of campaigns. Their customers, like LaserAway, had an impressive purchase ROAS lift of 59%, and Advanced Hair Restoration had a surge of 64%. In December 2025, Scowtt raised $12m in a Series A round led by Inspired Capital with participation from LiveRamp Ventures, Angeles Investors, and Angeles Ventures.
Seekr Careers - Candidate Matching at Scale
Seekr Careers had to get beyond keyword matching to find employees. One of the challenges with keyword matching in recruitment is that it's not about what the candidate says but how closely it matches the job description. A candidate who says exactly what's in the job description is likely to be ranked higher than one who says it in their own words. Agicent developed a recommendation model based on behavioural signals, such as view duration of a job, completion rates, scroll depths in particular role types and subsequent application behaviour.
The model learned what real interest was from the data instead of from similarity of text. This resulted in a tangible satisfaction of the enterprise recruiter clients by lowering the rate of false matches, and a benefit in the conversion of applications to interviews that the keyword approach could not structurally deliver.
GigzzApp - Dynamic Pricing and Supply-Demand Prediction
GigzzApp is an on-demand marketplace that has workers and clients whose availability and needs change in ways that standard pricing tables are not able to accommodate - either by keeping workers idle during low demand times or by keeping orders unfilled during peak demand times.
Agicent developed a demand sensing model that fed in information such as time of day, location, event calendar and booking patterns from its past to generate current demand forecasts and a dynamic pricing layer that used those forecasts to make rate adjustments to achieve a balance between marketplace liquidity and without manual intervention. The outcome was better "fill rates" during periods of high demand, and lower "idle time" during periods of low demand - both of which impact the unit economics of a marketplace business.
We build AI solutions for different sectors, and the work we do in each is driven by the needs of the industry. Here are the areas where we're delivering AI development.
Fintech and Banking
In financial services, machine learning realizes tangible value across three key areas: credit underwriting, fraud detection, and compliance with various regulatory requirements - and these areas are where there is not a simple tradeoff between accuracy and explainability. We have developed fraud detection models that are able to run at transaction speed on behavioural data, credit scoring models that leverage alternative data for borrowers with thin credit history, and document extraction pipelines that automate regulatory reporting workflows without the error rates associated with manual data entry. Our work on Tide 360 and GigzzApp demonstrates these capabilities in real-world production environments rather than in theory.
Healthtech and Clinical Applications
Healthcare ML operates in a landscape where data sensitivity, regulations, and patient impact are of paramount importance. We develop patient risk stratification models for care coordination software, NLP pipelines that automatically code medical notes and manage prior authorisation, appointment no-show prediction models for scheduling optimisation, and clinical trial matching systems that automatically match structured EMR fields and physician notes. All data architectures for healthtech engagements at Agicent are HIPAA compliant, like MediOrbis, and no model work is done before the data architecture is in place.
Edtech and Learning Platforms
The best thing an edtech platform can do with machine learning is to not show content that the learner has already mastered, and not withhold content that the learner is ready to see. We create adaptive learning recommendation engines that deliver the next learning object based on what the learner has already shown they can do; prediction models for student engagement to flag learners at risk of dropping off before they actually do and churn models for subscription based platforms, where the cost of a lost paid learner is great enough to warrant early intervention.
SaaS Products
SaaS businesses have the types of behavioural data that make machine learning truly useful, such as: Agicent develops churn prediction and customer health scoring models for product-led SaaS applications, semantic search applications to enhance in-app discovery, and usage anomaly detection applications for multi-tenant applications where per-customer monitoring is commercially viable. Seekr Careers is a typical representative of the recommendation and matching work for the vertical
E-commerce and Consumer Apps
But delivering personalisation at scale in e-commerce demands dealing with the cold start problem when a new user logs in, factoring in that purchase data is a biased subset of preference, and providing ranking results in real-time without latency to the end-user. We develop products that create product ranking and recommendations systems, as well as visual search and image similarity engines for catalogue discovery, and demand forecasting models that combine seasonality, promotional calendars and external demand signals together at once and not as independent forecasting problems.
Enterprise and Operations
Predictive maintenance for manufacturing and IoT-instrumented assets, document processing automation for contracts and invoices, and supply chain demand sensing are three enterprise ML applications where the return is measurable in operational cost reduction rather than revenue uplift. We have developed and implemented models in each of these categories, and the engineering problem they have in common is that they need to embed ML output into existing processes that predate the ML system itself and will continue to exist for each of these models - regardless of how many different versions they go through.
Engineering before models
We begin with data architecture and integration layer. If the data pipeline that supplies the model is inconsistent, then the model itself will be inconsistent, and if the model is performing well in the evaluation and there is not a stable, low-latency path to put the model's output in the product or workflow that requires it, then the model will be ignored.
Honest scoping
Our team turns down ML projects that are not ML problems. We tell you that when the discovery phase shows that a business question can be better answered and more cost effectively answered with a rules engine, a well-designed BI dashboard, or a prompt-engineered API call. This occurs in a significant number of initial meetings and those clients who hear it are likely to return with a more well-defined project in hand.
Cross-domain production history
Fintech fraud detection, healthtech risk stratification, edtech recommendation, SaaS churn prediction - cross-domain experience changes the quality of feature engineering decisions in ways that are hard to articulate but easy to see in model performance. The idea of predicting churn based on engagement signals in SaaS translates to e-learning dropouts.
MLOps from day one
The initial Agicent project scope involves building monitoring and drift detection/retraining infrastructure. Not a Phase 2. It is not a follow-on engagement when the model begins to degrade. The monitoring layer is designed at the time of designing the model.
Compliance-aware architecture
For healthtech and fintech clients, the compliance requirements shape the architecture from the data layer through inference. So we don't keep this in post-deployment compliance review - we keep it as an engineering input at the scoping stage.
If you are evaluating whether to build ML capabilities in-house or with a partner, or if you need help scoping a specific use case
Data governance and lineage
All ML pipelines built by Agicent include traceable lineage of data from its source to each data transformation step through to the model training run that used it. This is important in regulated industries where a compliance auditor, legal team, or regulatory body will eventually ask the question, "what data did this model train on?"
Privacy by design
PII handling is more of an engineering input to the data pipeline and not a layer added after the ML system is built. So before model development, we apply differential privacy techniques, anonymisation pipelines and role-based access controls to training data to protect health and financial data. Engineering decisions are made during scoping based on HIPAA, GDPR, and CCPA requirements, not when deploying the application.
Model transparency and explainability
Some applications are OK with black-box models and others are not OK with black-box models. In cases where a model's output is relevant to an individual context, like credit scoring, or a clinical risk prediction context, we incorporate interpretability into the model design and architecture from the beginning, not as an afterthought on an un-designed model. Depending on the model family and the regulatory environment, this can be SHAP values, LIME attribution or architecture-level decisions that sacrifice marginal accuracy gains for interpretability.
Bias auditing and fairness evaluation
The models are built based on historical data and some of the patterns it contains are discriminatory. So we conduct bias detection during the model evaluation stage, on holdout slices which are intended to simulate the real population the model will operate on. Fairness metrics are not identified as a concern after the production of the model, but rather are defined at the scoping stage, with the client in mind.
Our ML delivery covered HIPAA, GDPR, CCPA, SOC 2 Type II, ISO/IEC 27001 and the NIST AI Risk Management Framework. Agicent's compliance architecture work meets the needs of clients in the EU, whose building systems will come under the EU AI Act's high-risk application categories, at the design level.
Not all business issues are ML problems - when you make them one, you end up with systems that are super expensive and sub-optimal, which makes the investment in AI seem less valuable throughout an organisation. This is the beginning of Agicent's discovery process, before a model is suggested. If these three conditions are all true, it's the right time for machine learning.
First, the decision should get better as more data comes in. A rules-based system has static rules - either they are changed manually or remain unchanged. A model should improve its performance in the decision it was modeled for when tested on new data. If the problem you're trying to solve doesn't have that property, then ML gets more complicated and does not provide any benefit.
Second, the problem has too many interacting variables to be reliably solved by a set of rules written by a human. The reason fraud detection is an ML problem is that there are lots of signals, and they're correlated in not-so-obvious ways, and they're changing at a rate that exceeds a rule that can be written and tested.
Third, the number of decisions is such that an error rate cannot be maintained due to human review. In one of the thousands of documents processed every day, in content moderation for a social platform of millions of documents per day, or in real-time product ranking across millions of products per second, these are examples where ML is a necessity.
The most common misfits that Agicent encounters when a client first brings it up: a reporting problem that is presented as a prediction problem, where the business inquiry is really answered by data aggregation, instead of learning; a data problem where there isn't the history of data, examples of the data that is labelled, and the coverage of the edge cases that matter for a machine learning model to learn from; and a scope creep problem where a client says they need AI and they mean they need a better search bar, which is a retrieval engineering problem and not a machine learning project.
McKinsey's 2024 State of AI report stated that roughly half of companies that have deployed AI report at least one significant negative consequence from AI use - most commonly inaccurate outputs and data privacy issues.
In most of those cases, it is not a wrong model. It's a project that has been scoped without any consideration of whether there is data, use case, deployment context ready for ML. It is that assessment that is the result of our discovery phase and this is why we complete and adopt the projects we undertake better than industry average.
So, custom ML compounds need to be built over time in the right conditions, which cannot be matched by API integration and off-the-shelf tools. For most built ML systems, the competitive edge has to do with the proprietary data used to train the system and the production iteration history that has shaped the system to the patterns of a specific business, neither of which a vendor can copy nor a competitor can purchase.
Identifying and engaging POCs
A time-boxed scoping engagement to validate the ML use case, to examine the readiness of data, and to create a production roadmap and baseline level of accuracy. Ideal for organisations looking to prove the viability of an ML capability before investing a full development budget, or organisations assessing the feasibility of building an ML capability in-house or with a development partner. You will get a concrete plan, not a slide deck.
End-to-end ML development
The complete end-to-end data engineering to model deployment and MLOps setup. Agicent is the pipeline owner and delivery milestones owner. The client will be responsible for the business context, data access, and decisions on where the model's output fits into the existing workflow. Depending on data preparation and integration, a well scoped production deployment with clean data usually takes 3 to 4 months, while a more complex deployment with extensive data infrastructure work will take 5 to 6 months.
ML Engineer Team
For startups and businesses validating machine learning use cases
Billed Monthly
ML Product Team
For scaling machine learning products and production deployments
Billed Monthly
ML Growth Team
For advanced ML products, LLM applications, and enterprise-scale deployments
Billed Monthly
Staff augmentation services
For product companies and enterprises with their own engineering team and the need to bring in ML skills without the burden of a full external delivery project. Agicent employs data scientists, ML engineers, and MLOps engineers who operate on the client's sprint cycle, utilize the client's tools and are responsible to the client's engineering managers. This arrangement can start within two weeks of an agreed scope.
If you're not sure which of these applies to you at this moment, the best thing to do is to speak about it, not propose it. Discuss your use case for ML with the Agicent team.
Artificial Intelligence (AI)
For production ML systems, the AI layer is the point where outputs of the models meet the business logic. The probability returned by a model or a ranked list, or the extracted value, determines the next action of the AI layer: if it's high enough it can take action on its own; if it's not high enough, it can flag it for human review or send it to the rule layer before it gets to the application layer.
Agicent makes this boundary clear and traceable, especially for regulated applications where there is a model based decision with legal or clinical consequences that must be traceable.
Generative AI
Selection of the model for generative AI tasks at Agicent is not based on benchmarks but on deployment requirements. For clients who have data residency requirements on premise or budget considerations that make API access at production volume unviable, Open-weight models (such as LLaMA, Mistral, and Falcon) are the better option.
For cloud native products that prefer managed infrastructure over self-hosted, and where the difference in capability is not that significant, API-based models from Anthropic and OpenAI are more suitable. Prompt engineering and RAG architecture decisions are done at the model selection stage, as the prompt strategy that is effective for one family of models does not necessarily transfer to another family.
Agentic AI
The engineering challenge that makes agentic systems difficult to produce is not building the agent; it's keeping the agent coherent and recoverable across multi-step task sequences, where individual tool calls might fail, produce ambiguous results, or alter the meaning of later step calls.
Our agentic implementations use LangChain and LangGraph for orchestration, incorporate structured fallback logic for known failure modes, and explicitly consider human-in-the-loop escalation points for steps that represent business risk that is unquantifiable by the system. It's not a problem to be solved later; it's an engineering problem.
Computer Vision
The first step in computer vision model design at Agicent is the precision-recall tradeoff that the business impact of the error suggests. The cost of a miss on a manufacturing line is different from the cost of a false positive in a document classification pipeline, and that different cost leads to different operating thresholds and different designs for the human review layer that follows the model.
Even the same architecture families are deployed differently: a cloud deployable architecture for document processing in batch, and an edge deployable architecture for real-time inspection on a factory floor are both built on the same architecture families, but are optimized for vastly different compute and latency requirements.
Natural Language Processing (NLP)
There are three factors that determine NLP application architecture in our work: what the task needs the model to do with language, how much task-specific information is available to fine-tune the model and what latency and cost-per-inference constraints are imposed by the deployment environment. Unlike a general-purpose model, fine-tuned transformer models are suitable for domain-specific extraction tasks whose outputs are too noisy for a general-purpose model.
Embedding-based retrieval is the only solution that can deal with semantic search at scale without adding latency and without compromising quality. RAG pipelines are used for the generation tasks where the model needs to operate on a knowledge base that is too large to fine-tune against, or too often updated to be economically feasible to fine-tune. NLP is also where problems in annotation quality are more prevalent - the same inconsistent labels and vocabulary gaps in the domain are a greater threat to model errors that are seen in structured ML problems.
Data Mining
Data mining is the analytical activity that takes place before the development of the model in our ML process and that helps to decide whether to continue with the model development process. It includes pattern discovery in historical data, association rule learning for analysing behaviour, anomaly detection for quality control and fraud identification, and feature importance analysis to identify which signals in the data are truly predictive and which are not.
A comprehensive data mining step removes the non-predictive features to prevent running costly training passes before beginning, removes complexity from the model and often uncovers new information that alters the whole definition of the ML problem.
Deep Learning
Deep learning at Agicent is the choice of last resort in the best possible sense: when there simply is no better way to solve the problem than with deep learning, and when the application has specific requirements for the performance that classical ML can't manage. CNN architectures for image and video analysis, LSTM and RNN architectures for sequence modelling tasks in which the temporal dependency is important, and transformer architectures for language and multimodal applications, in which the relationship between inputs is not local.
It's true that Deep Learning is an engineering challenge, it needs more data, compute, time to train, and complex models that are less explainable and harder to retrain - and those are costs that must be balanced against the value of the capability it brings.
Robotic Process Automation (RPA)
RPA executes the deterministic downstream layer of the work in process automation done by us: the action that takes place after the decision is made by the ML model. Based on the category of the document, the model classifies it, or extracts the value of the field, or forwards the request into the target system - the RPA layer adds the data to the target system, initiates the workflow, or sends the notification.
The boundary between the ML layer and rules-based automation is established during the scoping phase since the cost and complexity of both layers are quite different and mixing them together results in too much engineering where it isn't needed.
Cloud
Cloud choice for Agicent's ML work depends on three inputs from the client: the current cloud footprint and familiarity with it that comes with it; data sovereignty and residency needs that can impact availability of regions and providers; and the cost structure of the anticipated inference volume.
All three of these managed ML infrastructure offerings, AWS SageMaker, Azure Machine Learning, and GCP Vertex AI, are well developed, and there are some distinct differences in the available features and tooling, as well as ecosystem integrations and pricing at scale. When enterprise-level infrastructure decisions change often, and the implementation of these decisions moves to the ML level, we make pipelines versatile at the architecture level, not just tightly bound to a single provider's managed service.
Big Data and Analytics
The infrastructure layer that ML pipelines run on at Agicent includes streaming data ingestion using Apache Kafka for those applications where the model must be applied to real-time data; batch processing using Apache Spark for the computation of large-scale features; and feature stores, such as Feast and Tecton, which ensure consistency between features a model was trained on and features it is applied to during inference.
This includes the BI output layer as well: BI dashboards and BI reporting integrations are designed with the model and not as an afterthought.
Ready to Build a Machine Learning Product? Validate your ML idea before development begins.
A scoped POC engagement with Agicent typically begins at approximately $20,000 and includes use case validation, data readiness assessment, and a baseline accuracy benchmark. Typically, full production deployments (data engineering, model development, integration, MLOps infrastructure) come in at $50,000 to $120,000 and higher, depending on the complexity of the data and the number of systems the model must interact with.
A POC will be completed in 4-8 weeks based on the availability of data. It takes 3-6 months to develop a production ML application with integration and a working MLOps layer. The most frequent reason for lengthier timelines is data that's not as ready as it was at the beginning of the deal.
AI is the broader category covering any system that exhibits intelligent behaviour. Machine learning is the subset where the system learns from data rather than following explicitly programmed rules. Most production AI applications - recommendation systems, fraud detection models, NLP pipelines - are machine learning applications at their technical core.
In nearly all instances, yes. The major source of competitive advantage in an ML system is the proprietary data a business has gathered. Sparse data sets can be supplemented with synthetic data generation or transfer learning from a pre-trained model, but a certain amount of labelled data is essential for a model that is expected to have acceptable accuracy and performance on the actual data within a real business scenario.
Honest Scoping that rejects projects for which data is insufficient. Production engineering that is not limited to model training, but also includes integration, deployment and monitoring. Infrastructure for MLOps as part of the initial scope - not as a follow-on. Not just built but working in production, and cross-domain shipping history that indicates the company has overcome the operational challenges of running ML in production, not just the research challenges of building it.
Yes. We deploy ML models as REST APIs, as event-driven inference services for applications that require acting on real-time data streams, and as embedded modules for applications with unacceptable latency for a network call. The integration architecture is defined during scoping, since it impacts design decisions in the model during training.
Fintech, healthtech, edtech, enterprise SaaS, e-commerce, and consumer apps. All of these verticals have different data constraints, regulations and user behaviour patterns that influence model architecture and deployment. Our cross-vertical experience means those constraints are identified during scoping rather than discovered during integration.